We evaluated the performance of TTUR based on SGD, Momentum, and Adam in training a deep convolutional GAN (DCGAN) [1]} on the CIFAR-10 dataset. The experimental environment consisted of two Intel(R) Xeon(R) Gold 6148 2.4-GHz CPUs with 20 cores each, a 16-GB NVIDIA Tesla V100 900-Gbps GPU, and Red Hat Enterprise Linux 7.6 OS. The code was written in Python 3.8.2 using the NumPy 1.17.3 and PyTorch 1.3.0 packages. TTUR based on SGD used the identity matrices \(\mathsf {H}_n^G\)  and \(\mathsf {H}_n^D\)  and \(\beta ^G = \beta ^D = \gamma ^G = \gamma ^D = 0\) , and TTUR based on Momentum used the identity matrices \(\mathsf {H}_n^G\)  and \(\mathsf {H}_n^D\) , \(\beta ^G = \beta ^D = 0.9\) , and \(\gamma ^G = \gamma ^D = 0\) . Adam used \(\mathsf {H}_n^G\)  and \(\mathsf {H}_n^D\)  defined in Table REF , \(\beta ^G = \beta ^D = \gamma ^G = \gamma ^D = 0.9\) , and \(\eta ^G = \eta ^D = \zeta ^G = \zeta ^D = 0.999\)  [2]}.
